•A novel method for modal parameter identification of arch dam based on multi-level information fusion is proposed.•The closely spaced and high-frequency modes are accurately identified.•The method ...can significantly improve the identification accuracy and has better applicability.
Operational modal analysis plays an important role in the structural health monitoring and safety diagnosis of arch dam. However, due to background noise, it is difficult to accurately extract the effective characteristics information of arch dam from the vibration responses whose amplitudes are too small under ambient vibration, and the deviation caused by traditional identification methods will directly affect the estimation accuracy for structural modal parameters. Therefore, a novel methodology for modal parameter identification of arch dam based on multi-level information fusion is proposed in this paper. The proposed method is based on multi-sensor data-level fusion to identify the structural natural frequency and damping ratio, which greatly preserves and extracts structural modal properties in the vibration responses. Meanwhile, structural mode shapes are identified based on dynamic feature-level fusion, which significantly improves the identification accuracy. The effectiveness and feasibility of the proposed method are verified by the modal results of digital signals and simulated signals in the 7-DOF system. Prototype engineering case shows that the closely spaced and high-frequency modes can be decomposed and identified by the proposed method, and this method has a higher identification accuracy, which can provide a new idea for modal parameter identification of arch dam.
An iterative noise extraction and elimination method is proposed, to solve the difficulty of modal parameter identification caused by contaminated high-energy components in measured signals. The ...approach is based on the idea that if only the high-energy noise is extracted and eliminated accurately, modal parameter identification can be improved significantly by using the remaining (filtered) signal. A theoretical development is that a general form of noises is considered, i.e., they can be purely harmonic or damped, which implies noises from machine vibration or other unknown sources can be taken into account. The advantage of this approach is that only one significant, relatively noisy component is extracted at each iteration, i.e., only the most reliable component is extracted each time, so more accurate high-energy noise elimination is expected. To demonstrate the accuracy of the proposed method, a numerical signal with one high-energy noisy component is used. Numerical results show that the approach can extract and eliminate high-energy noise at 2.1 Hz accurately by setting a small model order. A special case is the noisy component as a harmonic, which means the traditional Fourier transform can be applied. However, one can conclude that the proposed method outperforms the Fourier transform due to its limitation of fixed frequency resolution. To further investigate the effectiveness of the approach, an experiment on a steel offshore platform is conducted, and experimental results indicate that when measured raw data is used, only the first mode can be identified; if filtered data is used by employing the proposed method, the first two modes can be identified with improved damping ratio estimation. Finally, sea-test data from an offshore wind turbine in operating conditions are used, results show that the first mode with a frequency of 0.3592 Hz and a damping ratio of 0.0151 are successfully identified, while no reasonable parameters can be obtained by employing the stochastic subspace identification method, even when the used model order reaches 300.
•An iterative noise extraction and elimination method is proposed to deal with noise contaminated measured signals.•Interference of high-energy noise on modal parameter identification can be reduced significantly.•Results of an experimental offshore platform show an obvious improvement of modal parameter identification.•Sea test data of an offshore wind turbine is used to investigate performance of the approach.
This paper proposes a 2D spectral analysis method based on damped Capon (dCAPON) and damped APES (dAPES) for structural modal parameter identification. The frequency and damping are estimated from ...the 2D dCAPON energy spectrum first, and then the amplitude (to construct the modal shape) corresponding to the frequency-damping point is calculated by the dAPES. The raw evenly-spaced computing grid is optimized into a banded hierarchically-refined one, greatly improving the computational efficiency. The performance of the proposed method is studied and it is investigated through the numerical and experimental data of a benchmark structure. The proposed method provides a simple and direct approach to estimating structural modal parameters, particularly for frequency and damping ratio.
•A novel method for modal parameter identification is proposed.•It provides a direct inspection of modal parameters by domain transformation.•Efficient implementation based on banded hierarchically-refined computing grid.•The method is validated through a benchmark structure.
As the penetration of renewable energy increases to a large scale and power electronic devices become widespread, power systems are becoming prone to synchronous oscillations (SO). This event has a ...major impact on the stability of the power grid. The recent research has been mainly concentrated on identifying the parameters of sub-synchronous oscillation. Sub/Super synchronous oscillations (Sub/Sup-SO) simultaneously occur, increasing the difficulty in accurately identify the parameters of SO. This work presents a novel method for parameter identification that effectively handles the Sub/Sup-SO components by utilizing the Rife-Vincent window and discrete Fourier transform (DFT) simultaneously. To mitigate the impact of spectral leakage and the fence effect of DFT, we integrate the tri-spectral interpolation algorithm with the Rife–Vincent window. We use the instantaneous data of the phasor measurement unit (PMU) to identify Sub/Sup-SO-related parameters (Sub/Sup-SO damping ratio, frequency, amplitude and phase). First, the spectrum of the Sub/Sup-SO signals is analyzed after incorporating the Rife-Vincent window, and the characteristics of the Sub/Sup-SO signal are determined. Then, the signal spectrum is identified using a three-point interpolation algorithm, and the damping ratio, amplitude, frequency, and phase of the Sub/Sup-SO signals are obtained. In addition, we consider the identification accuracy of the algorithm under various complex conditions, such as the effect of Sub/Sup-SO parameter variations on parameter identification in the presence of a non-nominal frequency and noise. The proposed algorithm accurately identifies the parameters of multiple Sub/Sup-SO components and two Sub-SO components that are in close proximity. Testing with synthetic and real data demonstrates that the proposed algorithm outperforms existing methods in terms of identification accuracy, identification bandwidth, and adaptability.
•Sub/Sup-SO parameter identification is proposed via tri-spectral line interpolation.•This method obtains wide identification band range and high measurement accuracy.•Parameter identification is proposed for parameters containing Sub-SO and Sup-SO frequencies.•The deviation of parameter identification was less than 1 % in most cases.
In the context of wireless intelligent sensing and edge computing, a suitable way of modal parameter identification (MPI) for vibration monitoring of engineering structures is to use a distributed ...scheme where the majority of computing tasks are deployed to the sensor ends. This paper presents a time-domain algorithm called free-vibration-response based mode decomposition (FVRMD) for distributed MPI, which directly estimates frequency, damping ratio, initial amplitude, and initial phase from the vibration signals. Since FVRMD only relies on a single-channel input, it can be independently and synchronously deployed on each intelligent sensor in the distributed MPI scenario. Additionally, we provide a remote integration scheme to extract structure modal shape and the final representative frequency and damping ratio from the identified parameters at the edge. The proposed method is verified by analyzing vibrations in a simulated and experimental beam structure, as well as a real suspension bridge. Results demonstrate that the method is accurate and stable in estimating frequency and damping ratio, and its ability to identify modal shape is close to that of the centralized method.
•A distributed modal parameters identification method based on FVRMD is provided.•It is good at damped signal decomposition and MPI with sound noise robustness.•The obtained modal shape is close to that obtained by the centralized method.•It is flexible to be deployed on distributed structural monitoring systems.
•A new methodology for modal parameter identification of large civil structures.•It uses MUSIC-EWT algorithm and Hilbert transform.•It is applied to a 123-story highrise building structure, Lotte ...World Tower.•It is effective for modal parameter identification of superhighrise structures.•It can deal with noisy signals.
A key issue in health monitoring of smart structures is the estimation of modal parameters such as natural frequencies and damping ratios from acquired dynamic signals. In this article, a new methodology is presented for calculating the natural frequencies (NF) and damping ratios (DR) of large civil infrastructure from acquired dynamic signals using a multiple signal classification (MUSIC) algorithm, the empirical wavelet transform (EWT), and the Hilbert transform. The effectiveness of the proposed method is validated by means of three examples: a benchmark 3D 4-story steel frame structure, a benchmark problem, subjected to dynamic loading, an 8-story steel frame subjected to white noise input on a shaking table, and a 123-story highrise building structure, Lotte World Tower (LWT), under construction in Seoul, South Korea. The results demonstrate that the new methodology is accurate for estimating the NF and DR of a superhighrise building structure using low-amplitude ambient vibrations data, a complex and challenging task since the measured vibrations signals are noisy and present non-stationary characteristics. The new methodology can deal with noisy signals without degrading its ability to estimate the NF and DR of different one-of-a kind civil structures thus is particularly suitable for health monitoring of large smart structures under dynamic loading.
Modal identification is critical for structural condition monitoring. Variational mode decomposition (VMD) has been widely applied to identify modal parameters and has achieved excellent performance. ...It is crucial for VMD to predefine the decomposition parameters, that is, the mode number and balance factor. However, in practical engineering, abnormal impulses and heavy noise render it difficult to preset the mode number and balance factor. Therefore, a novel method, termed orthogonal and recursive VMD (ORVMD), is proposed to overcome the difficulty of setting decomposition parameters in advance. ORVMD consists of two components: recursive VMD (RVMD) and a rough-to-precise decomposition scheme based on an orthogonal algorithm. RVMD is an iterative method of VMD that is used to circumvent the difficulty of predefining the mode number. A rough-to-precise decomposition scheme based on an orthogonal algorithm is proposed to address the difficulty of setting the balance factor. Furthermore, the proposed ORVMD in combination with the Hilbert transform (HT) is employed to estimate the modal parameters of the structures. The raw signals are pre-processed by using the random decrement technique (RDT) to obtain its random decrement signature (RDS) and then the proposed method is applied to the RDS to identify the modal parameters of a simulated system and a real arch bridge. The obtained results show that the proposed method outperforms other existing methods in separating multicomponent signals; thus, it is an efficient method for identifying the natural frequencies and damping ratios of structures.
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•Full-field Experimental Modal Identification on high-speed videos.•LSCF eigenvalue stabilization from accelerometer data and LSFD modal constant from high-speed video.•High frequency ...(close to 10kHz) mode shapes.•Identification below the noise level.
Vibration measurements using optical full-field systems based on high-speed footage are typically heavily burdened by noise, as the displacement amplitudes of the vibrating structures are often very small (in the range of micrometers, depending on the structure). The modal information is troublesome to measure as the structure’s response is close to, or below, the noise level of the camera-based measurement system. This paper demonstrates modal parameter identification for such noisy measurements. It is shown that by using the Least-Squares Complex-Frequency method combined with the Least-Squares Frequency-Domain method, identification at high-frequencies is still possible. By additionally incorporating a more precise sensor to identify the eigenvalues, a hybrid accelerometer/high-speed camera mode shape identification is possible even below the noise floor. An accelerometer measurement is used to identify the eigenvalues, while the camera measurement is used to produce the full-field mode shapes close to 10kHz. The identified modal parameters improve the quality of the measured modal data and serve as a reduced model of the structure’s dynamics.
A new indirect strategy is proposed to estimate the bridge modal parameters from the dynamic responses of two vehicles using stochastic subspace identification technique. The effect of ambient ...excitation, such as ongoing traffic, is simulated as white-noise excitation at the bridge supports. The state-space model of the vehicle-bridge interaction system is derived for a single-degree-of-freedom quarter-car model and the bridge deck modeled as a simply-supported Euler-Bernoulli beam. Bridge modal frequencies can be estimated accurately from the vehicle responses. Two instrumented vehicles are required to estimate the bridge mode shapes, with one serving as a fixed reference sensor and the other as a moving sensor. The measured accelerations from the vehicles are divided into segments and each pair of signal segments forms a state-space identification problem. Local mode shape value from each signal segment can be estimated using the reference-based SSI method. A rescaling on the local mode shape values is applied to construct the global mode shapes. Effects of the bridge surface roughness, measurement noise and vehicle properties on the mode shape identification are also numerically studied. A vehicle-bridge interaction model in the laboratory serves for the experimental validation of the proposed strategy. Both numerical and experimental results show that the proposed method can estimate the bridge modal parameters with acceptable accuracy.
•A new approach to identify bridge modal parameters using passing instrumented vehicles has been developed.•Two instrumented vehicles serve as a fixed reference sensor and a moving sensor.•Bridge mode shapes are extracted using reference-based stochastic subspace identification.•The effect of ambient excitation is simulated as white-noise excitation at bridge supports.•The experimental study has been conducted to verify the proposed method.
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